Overview

Dataset statistics

Number of variables25
Number of observations924
Missing cells616
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory220.0 KiB
Average record size in memory243.8 B

Variable types

Numeric12
Categorical13

Alerts

buildUpPlaySpeed is highly overall correlated with buildUpPlaySpeedClassHigh correlation
buildUpPlayDribbling is highly overall correlated with buildUpPlayDribblingClassHigh correlation
buildUpPlayPassing is highly overall correlated with buildUpPlayPassingClassHigh correlation
chanceCreationPassing is highly overall correlated with chanceCreationPassingClassHigh correlation
chanceCreationCrossing is highly overall correlated with chanceCreationCrossingClassHigh correlation
chanceCreationShooting is highly overall correlated with chanceCreationShootingClassHigh correlation
defencePressure is highly overall correlated with defencePressureClass and 1 other fieldsHigh correlation
defenceAggression is highly overall correlated with defenceAggressionClassHigh correlation
defenceTeamWidth is highly overall correlated with defencePressureClass and 1 other fieldsHigh correlation
date is highly overall correlated with buildUpPlayDribblingClassHigh correlation
buildUpPlaySpeedClass is highly overall correlated with buildUpPlaySpeedHigh correlation
buildUpPlayDribblingClass is highly overall correlated with buildUpPlayDribbling and 1 other fieldsHigh correlation
buildUpPlayPassingClass is highly overall correlated with buildUpPlayPassingHigh correlation
chanceCreationPassingClass is highly overall correlated with chanceCreationPassingHigh correlation
chanceCreationCrossingClass is highly overall correlated with chanceCreationCrossingHigh correlation
chanceCreationShootingClass is highly overall correlated with chanceCreationShootingHigh correlation
defencePressureClass is highly overall correlated with defencePressure and 1 other fieldsHigh correlation
defenceAggressionClass is highly overall correlated with defenceAggressionHigh correlation
defenceTeamWidthClass is highly overall correlated with defencePressure and 1 other fieldsHigh correlation
buildUpPlayPositioningClass is highly imbalanced (71.0%)Imbalance
chanceCreationShootingClass is highly imbalanced (54.2%)Imbalance
defencePressureClass is highly imbalanced (54.1%)Imbalance
defenceAggressionClass is highly imbalanced (61.5%)Imbalance
defenceTeamWidthClass is highly imbalanced (62.0%)Imbalance
defenceDefenderLineClass is highly imbalanced (60.9%)Imbalance
buildUpPlayDribbling has 616 (66.7%) missing valuesMissing
date is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-02-28 18:30:49.972527
Analysis finished2023-02-28 18:31:09.059380
Duration19.09 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct924
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean708.34524
Minimum10
Maximum1450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:09.135398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile84.15
Q1325.75
median721.5
Q31064.25
95-th percentile1371.85
Maximum1450
Range1440
Interquartile range (IQR)738.5

Descriptive statistics

Standard deviation419.14413
Coefficient of variation (CV)0.59172294
Kurtosis-1.2504876
Mean708.34524
Median Absolute Deviation (MAD)371
Skewness0.051442403
Sum654511
Variance175681.8
MonotonicityStrictly increasing
2023-02-28T13:31:09.254424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1
 
0.1%
935 1
 
0.1%
937 1
 
0.1%
938 1
 
0.1%
939 1
 
0.1%
940 1
 
0.1%
950 1
 
0.1%
951 1
 
0.1%
952 1
 
0.1%
953 1
 
0.1%
Other values (914) 914
98.9%
ValueCountFrequency (%)
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
15 1
0.1%
16 1
0.1%
17 1
0.1%
18 1
0.1%
19 1
0.1%
ValueCountFrequency (%)
1450 1
0.1%
1449 1
0.1%
1448 1
0.1%
1447 1
0.1%
1446 1
0.1%
1445 1
0.1%
1433 1
0.1%
1432 1
0.1%
1431 1
0.1%
1430 1
0.1%

team_fifa_api_id
Real number (ℝ)

Distinct164
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13120.972
Minimum1
Maximum112409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:09.386455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q159
median244
Q31819
95-th percentile110636
Maximum112409
Range112408
Interquartile range (IQR)1760

Descriptive statistics

Standard deviation34707.921
Coefficient of variation (CV)2.6452249
Kurtosis3.9683939
Mean13120.972
Median Absolute Deviation (MAD)224
Skewness2.4371496
Sum12123778
Variance1.2046398 × 109
MonotonicityNot monotonic
2023-02-28T13:31:09.504481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614 6
 
0.6%
219 6
 
0.6%
69 6
 
0.6%
70 6
 
0.6%
1823 6
 
0.6%
71 6
 
0.6%
13 6
 
0.6%
72 6
 
0.6%
1792 6
 
0.6%
171 6
 
0.6%
Other values (154) 864
93.5%
ValueCountFrequency (%)
1 6
0.6%
2 6
0.6%
3 6
0.6%
4 6
0.6%
5 6
0.6%
7 6
0.6%
9 6
0.6%
10 6
0.6%
11 6
0.6%
12 6
0.6%
ValueCountFrequency (%)
112409 2
 
0.2%
112225 4
0.4%
111989 6
0.6%
111974 6
0.6%
111657 3
0.3%
111376 3
0.3%
111271 5
0.5%
111239 5
0.5%
110915 6
0.6%
110832 5
0.5%

team_api_id
Real number (ℝ)

Distinct164
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10116.647
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:09.626508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8121
Q18479
median8661
Q39864
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1385

Descriptive statistics

Standard deviation12290.762
Coefficient of variation (CV)1.2149047
Kurtosis173.60827
Mean10116.647
Median Absolute Deviation (MAD)483.5
Skewness12.57016
Sum9347782
Variance1.5106283 × 108
MonotonicityNot monotonic
2023-02-28T13:31:09.745536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8576 6
 
0.6%
8592 6
 
0.6%
9829 6
 
0.6%
10249 6
 
0.6%
8481 6
 
0.6%
9830 6
 
0.6%
10261 6
 
0.6%
9831 6
 
0.6%
9850 6
 
0.6%
8165 6
 
0.6%
Other values (154) 864
93.5%
ValueCountFrequency (%)
4087 5
0.5%
4170 3
0.3%
6269 4
0.4%
6391 2
 
0.2%
7794 5
0.5%
7819 6
0.6%
7869 6
0.6%
7878 5
0.5%
7943 6
0.6%
8121 6
0.6%
ValueCountFrequency (%)
208931 2
 
0.2%
108893 6
0.6%
10281 6
0.6%
10278 5
0.5%
10269 6
0.6%
10268 6
0.6%
10267 6
0.6%
10261 6
0.6%
10260 6
0.6%
10252 6
0.6%

date
Categorical

HIGH CORRELATION  UNIFORM 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
2011-02-22 00:00:00
156 
2012-02-22 00:00:00
156 
2014-09-19 00:00:00
155 
2013-09-20 00:00:00
153 
2015-09-10 00:00:00
153 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17556
Distinct characters10
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-02-22 00:00:00
2nd row2011-02-22 00:00:00
3rd row2012-02-22 00:00:00
4th row2013-09-20 00:00:00
5th row2014-09-19 00:00:00

Common Values

ValueCountFrequency (%)
2011-02-22 00:00:00 156
16.9%
2012-02-22 00:00:00 156
16.9%
2014-09-19 00:00:00 155
16.8%
2013-09-20 00:00:00 153
16.6%
2015-09-10 00:00:00 153
16.6%
2010-02-22 00:00:00 151
16.3%

Length

2023-02-28T13:31:09.846558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:09.942579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 924
50.0%
2011-02-22 156
 
8.4%
2012-02-22 156
 
8.4%
2014-09-19 155
 
8.4%
2013-09-20 153
 
8.3%
2015-09-10 153
 
8.3%
2010-02-22 151
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 7849
44.7%
2 2622
 
14.9%
- 1848
 
10.5%
: 1848
 
10.5%
1 1388
 
7.9%
924
 
5.3%
9 616
 
3.5%
4 155
 
0.9%
3 153
 
0.9%
5 153
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12936
73.7%
Dash Punctuation 1848
 
10.5%
Other Punctuation 1848
 
10.5%
Space Separator 924
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7849
60.7%
2 2622
 
20.3%
1 1388
 
10.7%
9 616
 
4.8%
4 155
 
1.2%
3 153
 
1.2%
5 153
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1848
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1848
100.0%
Space Separator
ValueCountFrequency (%)
924
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17556
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7849
44.7%
2 2622
 
14.9%
- 1848
 
10.5%
: 1848
 
10.5%
1 1388
 
7.9%
924
 
5.3%
9 616
 
3.5%
4 155
 
0.9%
3 153
 
0.9%
5 153
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7849
44.7%
2 2622
 
14.9%
- 1848
 
10.5%
: 1848
 
10.5%
1 1388
 
7.9%
924
 
5.3%
9 616
 
3.5%
4 155
 
0.9%
3 153
 
0.9%
5 153
 
0.9%

buildUpPlaySpeed
Real number (ℝ)

Distinct56
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.541126
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:10.064618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q146
median54
Q363
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.318181
Coefficient of variation (CV)0.2113923
Kurtosis-0.36443459
Mean53.541126
Median Absolute Deviation (MAD)8
Skewness-0.34374588
Sum49472
Variance128.10123
MonotonicityNot monotonic
2023-02-28T13:31:10.183645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 55
 
6.0%
65 50
 
5.4%
55 48
 
5.2%
60 46
 
5.0%
70 44
 
4.8%
45 44
 
4.8%
49 37
 
4.0%
48 36
 
3.9%
64 32
 
3.5%
63 29
 
3.1%
Other values (46) 503
54.4%
ValueCountFrequency (%)
20 3
 
0.3%
23 1
 
0.1%
24 3
 
0.3%
25 2
 
0.2%
26 2
 
0.2%
28 1
 
0.1%
29 2
 
0.2%
30 25
2.7%
31 2
 
0.2%
32 2
 
0.2%
ValueCountFrequency (%)
80 1
 
0.1%
78 2
 
0.2%
77 1
 
0.1%
76 3
 
0.3%
75 6
 
0.6%
74 1
 
0.1%
73 5
 
0.5%
72 4
 
0.4%
71 6
 
0.6%
70 44
4.8%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Balanced
766 
Fast
115 
Slow
 
43

Length

Max length8
Median length8
Mean length7.3160173
Min length4

Characters and Unicode

Total characters6760
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBalanced
2nd rowBalanced
3rd rowBalanced
4th rowBalanced
5th rowBalanced

Common Values

ValueCountFrequency (%)
Balanced 766
82.9%
Fast 115
 
12.4%
Slow 43
 
4.7%

Length

2023-02-28T13:31:10.302671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:10.397693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
balanced 766
82.9%
fast 115
 
12.4%
slow 43
 
4.7%

Most occurring characters

ValueCountFrequency (%)
a 1647
24.4%
l 809
12.0%
B 766
11.3%
n 766
11.3%
c 766
11.3%
e 766
11.3%
d 766
11.3%
F 115
 
1.7%
s 115
 
1.7%
t 115
 
1.7%
Other values (3) 129
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5836
86.3%
Uppercase Letter 924
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1647
28.2%
l 809
13.9%
n 766
13.1%
c 766
13.1%
e 766
13.1%
d 766
13.1%
s 115
 
2.0%
t 115
 
2.0%
o 43
 
0.7%
w 43
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
B 766
82.9%
F 115
 
12.4%
S 43
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1647
24.4%
l 809
12.0%
B 766
11.3%
n 766
11.3%
c 766
11.3%
e 766
11.3%
d 766
11.3%
F 115
 
1.7%
s 115
 
1.7%
t 115
 
1.7%
Other values (3) 129
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1647
24.4%
l 809
12.0%
B 766
11.3%
n 766
11.3%
c 766
11.3%
e 766
11.3%
d 766
11.3%
F 115
 
1.7%
s 115
 
1.7%
t 115
 
1.7%
Other values (3) 129
 
1.9%

buildUpPlayDribbling
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)15.3%
Missing616
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean48.373377
Minimum24
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:10.491714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile31.35
Q141
median49
Q355
95-th percentile67.65
Maximum77
Range53
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.66335
Coefficient of variation (CV)0.22043841
Kurtosis-0.43557957
Mean48.373377
Median Absolute Deviation (MAD)7
Skewness0.012177297
Sum14899
Variance113.70704
MonotonicityNot monotonic
2023-02-28T13:31:10.605740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
52 21
 
2.3%
48 19
 
2.1%
55 17
 
1.8%
53 15
 
1.6%
54 14
 
1.5%
36 10
 
1.1%
41 10
 
1.1%
51 10
 
1.1%
32 10
 
1.1%
56 10
 
1.1%
Other values (37) 172
 
18.6%
(Missing) 616
66.7%
ValueCountFrequency (%)
24 2
 
0.2%
26 1
 
0.1%
27 1
 
0.1%
28 3
 
0.3%
29 4
 
0.4%
30 1
 
0.1%
31 4
 
0.4%
32 10
1.1%
33 3
 
0.3%
34 8
0.9%
ValueCountFrequency (%)
77 1
 
0.1%
74 1
 
0.1%
71 3
 
0.3%
70 5
0.5%
69 5
0.5%
68 1
 
0.1%
67 1
 
0.1%
66 2
 
0.2%
65 3
 
0.3%
62 8
0.9%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Little
645 
Normal
262 
Lots
 
17

Length

Max length6
Median length6
Mean length5.9632035
Min length4

Characters and Unicode

Total characters5510
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLittle
2nd rowLittle
3rd rowLittle
4th rowLittle
5th rowNormal

Common Values

ValueCountFrequency (%)
Little 645
69.8%
Normal 262
28.4%
Lots 17
 
1.8%

Length

2023-02-28T13:31:10.710753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:10.805784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
little 645
69.8%
normal 262
28.4%
lots 17
 
1.8%

Most occurring characters

ValueCountFrequency (%)
t 1307
23.7%
l 907
16.5%
L 662
12.0%
i 645
11.7%
e 645
11.7%
o 279
 
5.1%
N 262
 
4.8%
r 262
 
4.8%
m 262
 
4.8%
a 262
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4586
83.2%
Uppercase Letter 924
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1307
28.5%
l 907
19.8%
i 645
14.1%
e 645
14.1%
o 279
 
6.1%
r 262
 
5.7%
m 262
 
5.7%
a 262
 
5.7%
s 17
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
L 662
71.6%
N 262
 
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5510
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1307
23.7%
l 907
16.5%
L 662
12.0%
i 645
11.7%
e 645
11.7%
o 279
 
5.1%
N 262
 
4.8%
r 262
 
4.8%
m 262
 
4.8%
a 262
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1307
23.7%
l 907
16.5%
L 662
12.0%
i 645
11.7%
e 645
11.7%
o 279
 
5.1%
N 262
 
4.8%
r 262
 
4.8%
m 262
 
4.8%
a 262
 
4.8%

buildUpPlayPassing
Real number (ℝ)

Distinct58
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.666667
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:10.904797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q140
median50
Q355
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.285103
Coefficient of variation (CV)0.23188568
Kurtosis-0.46441031
Mean48.666667
Median Absolute Deviation (MAD)7
Skewness0.1138595
Sum44968
Variance127.35356
MonotonicityNot monotonic
2023-02-28T13:31:11.021823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 80
 
8.7%
30 53
 
5.7%
35 47
 
5.1%
52 45
 
4.9%
55 42
 
4.5%
40 40
 
4.3%
48 40
 
4.3%
54 37
 
4.0%
45 34
 
3.7%
70 31
 
3.4%
Other values (48) 475
51.4%
ValueCountFrequency (%)
20 1
 
0.1%
22 1
 
0.1%
23 3
 
0.3%
24 2
 
0.2%
25 1
 
0.1%
26 3
 
0.3%
27 1
 
0.1%
28 1
 
0.1%
29 4
 
0.4%
30 53
5.7%
ValueCountFrequency (%)
80 1
 
0.1%
79 1
 
0.1%
77 1
 
0.1%
75 2
 
0.2%
74 1
 
0.1%
73 6
 
0.6%
72 5
 
0.5%
71 1
 
0.1%
70 31
3.4%
69 5
 
0.5%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Mixed
771 
Short
85 
Long
 
68

Length

Max length5
Median length5
Mean length4.9264069
Min length4

Characters and Unicode

Total characters4552
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMixed
2nd rowMixed
3rd rowMixed
4th rowMixed
5th rowMixed

Common Values

ValueCountFrequency (%)
Mixed 771
83.4%
Short 85
 
9.2%
Long 68
 
7.4%

Length

2023-02-28T13:31:11.128847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:11.221868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mixed 771
83.4%
short 85
 
9.2%
long 68
 
7.4%

Most occurring characters

ValueCountFrequency (%)
M 771
16.9%
i 771
16.9%
x 771
16.9%
e 771
16.9%
d 771
16.9%
o 153
 
3.4%
S 85
 
1.9%
h 85
 
1.9%
r 85
 
1.9%
t 85
 
1.9%
Other values (3) 204
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3628
79.7%
Uppercase Letter 924
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 771
21.3%
x 771
21.3%
e 771
21.3%
d 771
21.3%
o 153
 
4.2%
h 85
 
2.3%
r 85
 
2.3%
t 85
 
2.3%
n 68
 
1.9%
g 68
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
M 771
83.4%
S 85
 
9.2%
L 68
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 771
16.9%
i 771
16.9%
x 771
16.9%
e 771
16.9%
d 771
16.9%
o 153
 
3.4%
S 85
 
1.9%
h 85
 
1.9%
r 85
 
1.9%
t 85
 
1.9%
Other values (3) 204
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 771
16.9%
i 771
16.9%
x 771
16.9%
e 771
16.9%
d 771
16.9%
o 153
 
3.4%
S 85
 
1.9%
h 85
 
1.9%
r 85
 
1.9%
t 85
 
1.9%
Other values (3) 204
 
4.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Organised
877 
Free Form
 
47

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters8316
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganised
2nd rowOrganised
3rd rowOrganised
4th rowOrganised
5th rowOrganised

Common Values

ValueCountFrequency (%)
Organised 877
94.9%
Free Form 47
 
5.1%

Length

2023-02-28T13:31:11.298884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:11.381903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
organised 877
90.3%
free 47
 
4.8%
form 47
 
4.8%

Most occurring characters

ValueCountFrequency (%)
r 971
11.7%
e 971
11.7%
O 877
10.5%
g 877
10.5%
a 877
10.5%
n 877
10.5%
i 877
10.5%
s 877
10.5%
d 877
10.5%
F 94
 
1.1%
Other values (3) 141
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7298
87.8%
Uppercase Letter 971
 
11.7%
Space Separator 47
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 971
13.3%
e 971
13.3%
g 877
12.0%
a 877
12.0%
n 877
12.0%
i 877
12.0%
s 877
12.0%
d 877
12.0%
o 47
 
0.6%
m 47
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
O 877
90.3%
F 94
 
9.7%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8269
99.4%
Common 47
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 971
11.7%
e 971
11.7%
O 877
10.6%
g 877
10.6%
a 877
10.6%
n 877
10.6%
i 877
10.6%
s 877
10.6%
d 877
10.6%
F 94
 
1.1%
Other values (2) 94
 
1.1%
Common
ValueCountFrequency (%)
47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 971
11.7%
e 971
11.7%
O 877
10.5%
g 877
10.5%
a 877
10.5%
n 877
10.5%
i 877
10.5%
s 877
10.5%
d 877
10.5%
F 94
 
1.1%
Other values (3) 141
 
1.7%

chanceCreationPassing
Real number (ℝ)

Distinct49
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.498918
Minimum21
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:11.471923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35
Q146
median52
Q360
95-th percentile70
Maximum80
Range59
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.859738
Coefficient of variation (CV)0.20685642
Kurtosis-0.52282583
Mean52.498918
Median Absolute Deviation (MAD)7
Skewness-0.064151094
Sum48509
Variance117.93391
MonotonicityNot monotonic
2023-02-28T13:31:11.591951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50 66
 
7.1%
49 62
 
6.7%
52 53
 
5.7%
70 46
 
5.0%
55 45
 
4.9%
53 44
 
4.8%
65 37
 
4.0%
48 34
 
3.7%
45 33
 
3.6%
35 33
 
3.6%
Other values (39) 471
51.0%
ValueCountFrequency (%)
21 1
 
0.1%
28 2
 
0.2%
30 28
3.0%
31 1
 
0.1%
32 3
 
0.3%
33 1
 
0.1%
34 7
 
0.8%
35 33
3.6%
36 7
 
0.8%
37 11
 
1.2%
ValueCountFrequency (%)
80 1
 
0.1%
77 4
 
0.4%
76 1
 
0.1%
73 2
 
0.2%
72 10
 
1.1%
71 7
 
0.8%
70 46
5.0%
69 12
 
1.3%
68 28
3.0%
67 16
 
1.7%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Normal
761 
Risky
127 
Safe
 
36

Length

Max length6
Median length6
Mean length5.784632
Min length4

Characters and Unicode

Total characters5345
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 761
82.4%
Risky 127
 
13.7%
Safe 36
 
3.9%

Length

2023-02-28T13:31:11.934029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:12.033051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 761
82.4%
risky 127
 
13.7%
safe 36
 
3.9%

Most occurring characters

ValueCountFrequency (%)
a 797
14.9%
N 761
14.2%
o 761
14.2%
r 761
14.2%
m 761
14.2%
l 761
14.2%
R 127
 
2.4%
i 127
 
2.4%
s 127
 
2.4%
k 127
 
2.4%
Other values (4) 235
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4421
82.7%
Uppercase Letter 924
 
17.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 797
18.0%
o 761
17.2%
r 761
17.2%
m 761
17.2%
l 761
17.2%
i 127
 
2.9%
s 127
 
2.9%
k 127
 
2.9%
y 127
 
2.9%
f 36
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 761
82.4%
R 127
 
13.7%
S 36
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5345
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 797
14.9%
N 761
14.2%
o 761
14.2%
r 761
14.2%
m 761
14.2%
l 761
14.2%
R 127
 
2.4%
i 127
 
2.4%
s 127
 
2.4%
k 127
 
2.4%
Other values (4) 235
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 797
14.9%
N 761
14.2%
o 761
14.2%
r 761
14.2%
m 761
14.2%
l 761
14.2%
R 127
 
2.4%
i 127
 
2.4%
s 127
 
2.4%
k 127
 
2.4%
Other values (4) 235
 
4.4%

chanceCreationCrossing
Real number (ℝ)

Distinct55
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.971861
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:12.127072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q147
median54
Q363
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.388694
Coefficient of variation (CV)0.21101169
Kurtosis-0.41974502
Mean53.971861
Median Absolute Deviation (MAD)8
Skewness-0.26491844
Sum49870
Variance129.70235
MonotonicityNot monotonic
2023-02-28T13:31:12.243098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 58
 
6.3%
70 56
 
6.1%
54 49
 
5.3%
52 49
 
5.3%
65 48
 
5.2%
60 42
 
4.5%
55 34
 
3.7%
53 31
 
3.4%
51 29
 
3.1%
45 27
 
2.9%
Other values (45) 501
54.2%
ValueCountFrequency (%)
20 2
 
0.2%
23 1
 
0.1%
24 1
 
0.1%
25 3
 
0.3%
26 2
 
0.2%
27 1
 
0.1%
30 16
1.7%
31 5
 
0.5%
33 5
 
0.5%
34 17
1.8%
ValueCountFrequency (%)
80 2
 
0.2%
78 3
 
0.3%
77 3
 
0.3%
76 4
 
0.4%
75 1
 
0.1%
74 3
 
0.3%
73 8
 
0.9%
72 10
 
1.1%
71 7
 
0.8%
70 56
6.1%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Normal
745 
Lots
143 
Little
 
36

Length

Max length6
Median length6
Mean length5.6904762
Min length4

Characters and Unicode

Total characters5258
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 745
80.6%
Lots 143
 
15.5%
Little 36
 
3.9%

Length

2023-02-28T13:31:12.353122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:12.448144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 745
80.6%
lots 143
 
15.5%
little 36
 
3.9%

Most occurring characters

ValueCountFrequency (%)
o 888
16.9%
l 781
14.9%
N 745
14.2%
r 745
14.2%
m 745
14.2%
a 745
14.2%
t 215
 
4.1%
L 179
 
3.4%
s 143
 
2.7%
i 36
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4334
82.4%
Uppercase Letter 924
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 888
20.5%
l 781
18.0%
r 745
17.2%
m 745
17.2%
a 745
17.2%
t 215
 
5.0%
s 143
 
3.3%
i 36
 
0.8%
e 36
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 745
80.6%
L 179
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5258
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 888
16.9%
l 781
14.9%
N 745
14.2%
r 745
14.2%
m 745
14.2%
a 745
14.2%
t 215
 
4.1%
L 179
 
3.4%
s 143
 
2.7%
i 36
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 888
16.9%
l 781
14.9%
N 745
14.2%
r 745
14.2%
m 745
14.2%
a 745
14.2%
t 215
 
4.1%
L 179
 
3.4%
s 143
 
2.7%
i 36
 
0.7%

chanceCreationShooting
Real number (ℝ)

Distinct54
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.248918
Minimum22
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:12.543166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile35.15
Q149
median54
Q363
95-th percentile70
Maximum80
Range58
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.548179
Coefficient of variation (CV)0.19444036
Kurtosis-0.21944756
Mean54.248918
Median Absolute Deviation (MAD)6
Skewness-0.18422415
Sum50126
Variance111.26408
MonotonicityNot monotonic
2023-02-28T13:31:12.658191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 81
 
8.8%
70 69
 
7.5%
52 60
 
6.5%
55 58
 
6.3%
65 51
 
5.5%
53 43
 
4.7%
60 35
 
3.8%
56 34
 
3.7%
48 34
 
3.7%
49 30
 
3.2%
Other values (44) 429
46.4%
ValueCountFrequency (%)
22 1
 
0.1%
23 4
 
0.4%
24 1
 
0.1%
29 2
 
0.2%
30 5
 
0.5%
31 1
 
0.1%
32 3
 
0.3%
33 1
 
0.1%
34 9
1.0%
35 20
2.2%
ValueCountFrequency (%)
80 4
 
0.4%
79 1
 
0.1%
78 1
 
0.1%
77 1
 
0.1%
76 1
 
0.1%
75 4
 
0.4%
73 5
 
0.5%
72 10
 
1.1%
71 2
 
0.2%
70 69
7.5%

chanceCreationShootingClass
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Normal
774 
Lots
132 
Little
 
18

Length

Max length6
Median length6
Mean length5.7142857
Min length4

Characters and Unicode

Total characters5280
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 774
83.8%
Lots 132
 
14.3%
Little 18
 
1.9%

Length

2023-02-28T13:31:12.771217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:12.868239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 774
83.8%
lots 132
 
14.3%
little 18
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 906
17.2%
l 792
15.0%
N 774
14.7%
r 774
14.7%
m 774
14.7%
a 774
14.7%
t 168
 
3.2%
L 150
 
2.8%
s 132
 
2.5%
i 18
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4356
82.5%
Uppercase Letter 924
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 906
20.8%
l 792
18.2%
r 774
17.8%
m 774
17.8%
a 774
17.8%
t 168
 
3.9%
s 132
 
3.0%
i 18
 
0.4%
e 18
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 774
83.8%
L 150
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5280
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 906
17.2%
l 792
15.0%
N 774
14.7%
r 774
14.7%
m 774
14.7%
a 774
14.7%
t 168
 
3.2%
L 150
 
2.8%
s 132
 
2.5%
i 18
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 906
17.2%
l 792
15.0%
N 774
14.7%
r 774
14.7%
m 774
14.7%
a 774
14.7%
t 168
 
3.2%
L 150
 
2.8%
s 132
 
2.5%
i 18
 
0.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Organised
809 
Free Form
115 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters8316
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganised
2nd rowOrganised
3rd rowOrganised
4th rowOrganised
5th rowOrganised

Common Values

ValueCountFrequency (%)
Organised 809
87.6%
Free Form 115
 
12.4%

Length

2023-02-28T13:31:12.945256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:13.030275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
organised 809
77.9%
free 115
 
11.1%
form 115
 
11.1%

Most occurring characters

ValueCountFrequency (%)
r 1039
12.5%
e 1039
12.5%
O 809
9.7%
g 809
9.7%
a 809
9.7%
n 809
9.7%
i 809
9.7%
s 809
9.7%
d 809
9.7%
F 230
 
2.8%
Other values (3) 345
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7162
86.1%
Uppercase Letter 1039
 
12.5%
Space Separator 115
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1039
14.5%
e 1039
14.5%
g 809
11.3%
a 809
11.3%
n 809
11.3%
i 809
11.3%
s 809
11.3%
d 809
11.3%
o 115
 
1.6%
m 115
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
O 809
77.9%
F 230
 
22.1%
Space Separator
ValueCountFrequency (%)
115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8201
98.6%
Common 115
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1039
12.7%
e 1039
12.7%
O 809
9.9%
g 809
9.9%
a 809
9.9%
n 809
9.9%
i 809
9.9%
s 809
9.9%
d 809
9.9%
F 230
 
2.8%
Other values (2) 230
 
2.8%
Common
ValueCountFrequency (%)
115
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1039
12.5%
e 1039
12.5%
O 809
9.7%
g 809
9.7%
a 809
9.7%
n 809
9.7%
i 809
9.7%
s 809
9.7%
d 809
9.7%
F 230
 
2.8%
Other values (3) 345
 
4.1%

defencePressure
Real number (ℝ)

Distinct48
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.968615
Minimum23
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:13.119295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile30
Q138
median45
Q351
95-th percentile66
Maximum72
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.294372
Coefficient of variation (CV)0.22394349
Kurtosis-0.12293566
Mean45.968615
Median Absolute Deviation (MAD)6
Skewness0.42351891
Sum42475
Variance105.9741
MonotonicityNot monotonic
2023-02-28T13:31:13.238322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
45 79
 
8.5%
47 52
 
5.6%
35 52
 
5.6%
30 45
 
4.9%
50 42
 
4.5%
70 40
 
4.3%
49 39
 
4.2%
40 38
 
4.1%
48 35
 
3.8%
39 35
 
3.8%
Other values (38) 467
50.5%
ValueCountFrequency (%)
23 3
 
0.3%
24 2
 
0.2%
25 6
 
0.6%
26 3
 
0.3%
27 2
 
0.2%
28 2
 
0.2%
29 3
 
0.3%
30 45
4.9%
31 4
 
0.4%
32 3
 
0.3%
ValueCountFrequency (%)
72 1
 
0.1%
70 40
4.3%
68 3
 
0.3%
67 1
 
0.1%
66 3
 
0.3%
65 13
 
1.4%
64 6
 
0.6%
63 8
 
0.9%
62 3
 
0.3%
61 7
 
0.8%

defencePressureClass
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Medium
791 
Deep
88 
High
 
45

Length

Max length6
Median length6
Mean length5.7121212
Min length4

Characters and Unicode

Total characters5278
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeep
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 791
85.6%
Deep 88
 
9.5%
High 45
 
4.9%

Length

2023-02-28T13:31:13.345346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:13.442368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 791
85.6%
deep 88
 
9.5%
high 45
 
4.9%

Most occurring characters

ValueCountFrequency (%)
e 967
18.3%
i 836
15.8%
M 791
15.0%
d 791
15.0%
u 791
15.0%
m 791
15.0%
D 88
 
1.7%
p 88
 
1.7%
H 45
 
0.9%
g 45
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4354
82.5%
Uppercase Letter 924
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 967
22.2%
i 836
19.2%
d 791
18.2%
u 791
18.2%
m 791
18.2%
p 88
 
2.0%
g 45
 
1.0%
h 45
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
M 791
85.6%
D 88
 
9.5%
H 45
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 5278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 967
18.3%
i 836
15.8%
M 791
15.0%
d 791
15.0%
u 791
15.0%
m 791
15.0%
D 88
 
1.7%
p 88
 
1.7%
H 45
 
0.9%
g 45
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 967
18.3%
i 836
15.8%
M 791
15.0%
d 791
15.0%
u 791
15.0%
m 791
15.0%
D 88
 
1.7%
p 88
 
1.7%
H 45
 
0.9%
g 45
 
0.9%

defenceAggression
Real number (ℝ)

Distinct46
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.742424
Minimum27
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:13.539445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34
Q144
median49
Q355
95-th percentile70
Maximum72
Range45
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.4380972
Coefficient of variation (CV)0.18973939
Kurtosis-0.067280966
Mean49.742424
Median Absolute Deviation (MAD)6
Skewness0.27455522
Sum45962
Variance89.077678
MonotonicityNot monotonic
2023-02-28T13:31:13.652484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
45 85
 
9.2%
55 58
 
6.3%
50 58
 
6.3%
70 53
 
5.7%
47 50
 
5.4%
44 44
 
4.8%
52 40
 
4.3%
48 40
 
4.3%
46 38
 
4.1%
60 35
 
3.8%
Other values (36) 423
45.8%
ValueCountFrequency (%)
27 1
 
0.1%
28 1
 
0.1%
29 1
 
0.1%
30 28
3.0%
31 2
 
0.2%
32 2
 
0.2%
33 3
 
0.3%
34 14
1.5%
35 12
1.3%
36 1
 
0.1%
ValueCountFrequency (%)
72 1
 
0.1%
71 1
 
0.1%
70 53
5.7%
69 1
 
0.1%
68 2
 
0.2%
67 7
 
0.8%
66 4
 
0.4%
65 22
2.4%
64 2
 
0.2%
63 6
 
0.6%

defenceAggressionClass
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Press
821 
Double
 
65
Contain
 
38

Length

Max length7
Median length5
Mean length5.1525974
Min length5

Characters and Unicode

Total characters4761
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDouble
2nd rowPress
3rd rowPress
4th rowPress
5th rowPress

Common Values

ValueCountFrequency (%)
Press 821
88.9%
Double 65
 
7.0%
Contain 38
 
4.1%

Length

2023-02-28T13:31:13.761513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:13.861557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
press 821
88.9%
double 65
 
7.0%
contain 38
 
4.1%

Most occurring characters

ValueCountFrequency (%)
s 1642
34.5%
e 886
18.6%
P 821
17.2%
r 821
17.2%
o 103
 
2.2%
n 76
 
1.6%
D 65
 
1.4%
u 65
 
1.4%
b 65
 
1.4%
l 65
 
1.4%
Other values (4) 152
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3837
80.6%
Uppercase Letter 924
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1642
42.8%
e 886
23.1%
r 821
21.4%
o 103
 
2.7%
n 76
 
2.0%
u 65
 
1.7%
b 65
 
1.7%
l 65
 
1.7%
t 38
 
1.0%
a 38
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P 821
88.9%
D 65
 
7.0%
C 38
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4761
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1642
34.5%
e 886
18.6%
P 821
17.2%
r 821
17.2%
o 103
 
2.2%
n 76
 
1.6%
D 65
 
1.4%
u 65
 
1.4%
b 65
 
1.4%
l 65
 
1.4%
Other values (4) 152
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1642
34.5%
e 886
18.6%
P 821
17.2%
r 821
17.2%
o 103
 
2.2%
n 76
 
1.6%
D 65
 
1.4%
u 65
 
1.4%
b 65
 
1.4%
l 65
 
1.4%
Other values (4) 152
 
3.2%

defenceTeamWidth
Real number (ℝ)

Distinct42
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.270563
Minimum29
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.4 KiB
2023-02-28T13:31:13.953578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile35
Q148
median52
Q358
95-th percentile69
Maximum73
Range44
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.3459083
Coefficient of variation (CV)0.17879869
Kurtosis-0.14458383
Mean52.270563
Median Absolute Deviation (MAD)5
Skewness-0.10509277
Sum48298
Variance87.346001
MonotonicityNot monotonic
2023-02-28T13:31:14.064603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
50 99
 
10.7%
52 49
 
5.3%
65 49
 
5.3%
53 48
 
5.2%
51 48
 
5.2%
49 45
 
4.9%
55 45
 
4.9%
70 44
 
4.8%
54 39
 
4.2%
45 38
 
4.1%
Other values (32) 420
45.5%
ValueCountFrequency (%)
29 1
 
0.1%
30 23
2.5%
32 2
 
0.2%
33 2
 
0.2%
34 3
 
0.3%
35 22
2.4%
36 6
 
0.6%
37 5
 
0.5%
38 9
 
1.0%
39 15
1.6%
ValueCountFrequency (%)
73 1
 
0.1%
70 44
4.8%
69 5
 
0.5%
68 15
 
1.6%
67 11
 
1.2%
66 10
 
1.1%
65 49
5.3%
64 7
 
0.8%
63 5
 
0.5%
62 18
 
1.9%

defenceTeamWidthClass
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Normal
820 
Wide
 
76
Narrow
 
28

Length

Max length6
Median length6
Mean length5.8354978
Min length4

Characters and Unicode

Total characters5392
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNarrow
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 820
88.7%
Wide 76
 
8.2%
Narrow 28
 
3.0%

Length

2023-02-28T13:31:14.171627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:14.267648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 820
88.7%
wide 76
 
8.2%
narrow 28
 
3.0%

Most occurring characters

ValueCountFrequency (%)
r 876
16.2%
N 848
15.7%
o 848
15.7%
a 848
15.7%
m 820
15.2%
l 820
15.2%
W 76
 
1.4%
i 76
 
1.4%
d 76
 
1.4%
e 76
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4468
82.9%
Uppercase Letter 924
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 876
19.6%
o 848
19.0%
a 848
19.0%
m 820
18.4%
l 820
18.4%
i 76
 
1.7%
d 76
 
1.7%
e 76
 
1.7%
w 28
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 848
91.8%
W 76
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 876
16.2%
N 848
15.7%
o 848
15.7%
a 848
15.7%
m 820
15.2%
l 820
15.2%
W 76
 
1.4%
i 76
 
1.4%
d 76
 
1.4%
e 76
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 876
16.2%
N 848
15.7%
o 848
15.7%
a 848
15.7%
m 820
15.2%
l 820
15.2%
W 76
 
1.4%
i 76
 
1.4%
d 76
 
1.4%
e 76
 
1.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
Cover
853 
Offside Trap
 
71

Length

Max length12
Median length5
Mean length5.5378788
Min length5

Characters and Unicode

Total characters5117
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffside Trap
2nd rowCover
3rd rowCover
4th rowCover
5th rowCover

Common Values

ValueCountFrequency (%)
Cover 853
92.3%
Offside Trap 71
 
7.7%

Length

2023-02-28T13:31:14.348666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:31:14.442686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cover 853
85.7%
offside 71
 
7.1%
trap 71
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e 924
18.1%
r 924
18.1%
C 853
16.7%
o 853
16.7%
v 853
16.7%
f 142
 
2.8%
O 71
 
1.4%
s 71
 
1.4%
i 71
 
1.4%
d 71
 
1.4%
Other values (4) 284
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4051
79.2%
Uppercase Letter 995
 
19.4%
Space Separator 71
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 924
22.8%
r 924
22.8%
o 853
21.1%
v 853
21.1%
f 142
 
3.5%
s 71
 
1.8%
i 71
 
1.8%
d 71
 
1.8%
a 71
 
1.8%
p 71
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
C 853
85.7%
O 71
 
7.1%
T 71
 
7.1%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5046
98.6%
Common 71
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 924
18.3%
r 924
18.3%
C 853
16.9%
o 853
16.9%
v 853
16.9%
f 142
 
2.8%
O 71
 
1.4%
s 71
 
1.4%
i 71
 
1.4%
d 71
 
1.4%
Other values (3) 213
 
4.2%
Common
ValueCountFrequency (%)
71
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 924
18.1%
r 924
18.1%
C 853
16.7%
o 853
16.7%
v 853
16.7%
f 142
 
2.8%
O 71
 
1.4%
s 71
 
1.4%
i 71
 
1.4%
d 71
 
1.4%
Other values (4) 284
 
5.6%

Interactions

2023-02-28T13:31:06.787870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:51.666144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.017379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.330678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.662013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.257968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.324207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.782475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.188797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.867088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.233339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.508626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.903896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:51.792037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.129404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.444721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.785039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.350989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.446235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.904502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.316722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.983115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.341363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.622653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.011920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:51.901063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.238432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.556761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.899065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.439008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.560260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.021528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.430750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.100141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.453389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.732677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.114944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.007086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.343456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.665786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.007089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.524027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.671286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.136554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.541776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.214166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.555412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.837702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.228969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.120111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.460481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.780811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.120114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.611047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.787312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.255581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.661803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.333200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.664436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.947725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.555043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.216199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.556504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.868831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.218137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.697066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.915342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.360605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.753823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.424213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.752457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.032745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.662067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.332226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.667529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.988860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.335163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.788086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.051374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.481632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.116906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.542240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.859482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.140769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.769091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.452252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.779554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.107887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.451190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.879107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.176400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.603660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.287958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.659211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.967505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.252794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.878116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.568279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:53.892579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.226916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.807863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.972128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.302428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.722691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.407985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.779237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.081530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.363775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:07.987140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.683305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.004604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.339939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:56.926889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.066149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.428455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.844719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.524012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:03.893262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.190555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.474801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:08.091163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.791329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.109629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.444963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.036918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.152169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.554485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:00.957744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.643038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.004288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.301580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.580824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:08.195186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:52.908355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:54.217653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:55.551989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:57.148943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:58.238188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:30:59.671440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:01.071769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:02.756063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:04.114313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:05.401602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:31:06.684847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-28T13:31:14.548712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
idteam_fifa_api_idteam_api_idbuildUpPlaySpeedbuildUpPlayDribblingbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthdatebuildUpPlaySpeedClassbuildUpPlayDribblingClassbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingClasschanceCreationCrossingClasschanceCreationShootingClasschanceCreationPositioningClassdefencePressureClassdefenceAggressionClassdefenceTeamWidthClassdefenceDefenderLineClass
id1.000-0.0250.138-0.0060.089-0.0230.0730.1060.0170.048-0.016-0.0030.0000.0820.0000.0790.1700.0980.0800.0520.1420.0000.0710.0160.090
team_fifa_api_id-0.0251.000-0.141-0.0820.0240.0380.022-0.0540.006-0.109-0.0530.0500.0000.0000.0000.0000.0000.0000.0000.0570.0710.0660.0000.0130.036
team_api_id0.138-0.1411.000-0.018-0.062-0.020-0.008-0.035-0.051-0.015-0.024-0.0430.0000.0790.0260.0220.0000.0290.0000.0460.0000.0470.0000.0000.000
buildUpPlaySpeed-0.006-0.082-0.0181.000-0.0160.2940.2930.1940.1440.0420.148-0.0450.2020.9190.0930.3780.0820.2260.2030.1580.1330.1940.3400.1500.203
buildUpPlayDribbling0.0890.024-0.062-0.0161.000-0.1180.0460.0520.0910.0050.0230.0730.1120.0430.9330.0000.1200.1740.1510.2310.0000.1460.0000.0000.229
buildUpPlayPassing-0.0230.038-0.0200.294-0.1181.0000.2360.236-0.096-0.1130.0710.0020.2340.3800.0940.9240.3160.3060.3010.1440.2650.2370.3110.1540.391
chanceCreationPassing0.0730.022-0.0080.2930.0460.2361.0000.2480.1000.2300.1540.1070.1990.2980.1420.3170.1430.9250.2800.1000.1600.2300.2900.2040.182
chanceCreationCrossing0.106-0.054-0.0350.1940.0520.2360.2481.0000.0210.1050.1070.1010.1990.1970.1410.3050.1260.2980.9070.1050.1840.1790.2650.1710.123
chanceCreationShooting0.0170.006-0.0510.1440.091-0.0960.1000.0211.0000.1690.1020.1500.2290.1710.1180.1800.2660.1220.1280.9500.2290.2150.1640.1650.246
defencePressure0.048-0.109-0.0150.0420.005-0.1130.2300.1050.1691.0000.2600.4150.2590.1820.1250.2380.1090.2640.1530.2280.1390.9470.3200.5210.194
defenceAggression-0.016-0.053-0.0240.1480.0230.0710.1540.1070.1020.2601.0000.0950.2920.2910.1410.3010.2280.2720.2050.1480.1430.3260.9420.2910.358
defenceTeamWidth-0.0030.050-0.043-0.0450.0730.0020.1070.1010.1500.4150.0951.0000.2790.1540.1240.1990.1230.1890.1750.1550.1240.5650.2990.9290.203
date0.0000.0000.0000.2020.1120.2340.1990.1990.2290.2590.2920.2791.0000.1860.6560.2880.1680.2300.1740.2610.1310.3510.3850.3110.398
buildUpPlaySpeedClass0.0820.0000.0790.9190.0430.3800.2980.1970.1710.1820.2910.1540.1861.0000.0700.3530.0860.2610.1890.1340.1260.1300.2760.0820.162
buildUpPlayDribblingClass0.0000.0000.0260.0930.9330.0940.1420.1410.1180.1250.1410.1240.6560.0701.0000.0400.0760.0560.0580.1010.0640.1000.1120.1150.152
buildUpPlayPassingClass0.0790.0000.0220.3780.0000.9240.3170.3050.1800.2380.3010.1990.2880.3530.0401.0000.2830.2910.2660.1450.2130.1980.2700.1150.329
buildUpPlayPositioningClass0.1700.0000.0000.0820.1200.3160.1430.1260.2660.1090.2280.1230.1680.0860.0760.2831.0000.0400.0700.2310.3970.0710.1430.0690.236
chanceCreationPassingClass0.0980.0000.0290.2260.1740.3060.9250.2980.1220.2640.2720.1890.2300.2610.0560.2910.0401.0000.2490.1070.0990.1760.2310.1550.153
chanceCreationCrossingClass0.0800.0000.0000.2030.1510.3010.2800.9070.1280.1530.2050.1750.1740.1890.0580.2660.0700.2491.0000.0660.1400.1250.1890.1610.066
chanceCreationShootingClass0.0520.0570.0460.1580.2310.1440.1000.1050.9500.2280.1480.1550.2610.1340.1010.1450.2310.1070.0661.0000.2130.1960.1150.1210.181
chanceCreationPositioningClass0.1420.0710.0000.1330.0000.2650.1600.1840.2290.1390.1430.1240.1310.1260.0640.2130.3970.0990.1400.2131.0000.0000.0390.0000.152
defencePressureClass0.0000.0660.0470.1940.1460.2370.2300.1790.2150.9470.3260.5650.3510.1300.1000.1980.0710.1760.1250.1960.0001.0000.2920.4820.133
defenceAggressionClass0.0710.0000.0000.3400.0000.3110.2900.2650.1640.3200.9420.2990.3850.2760.1120.2700.1430.2310.1890.1150.0390.2921.0000.2410.326
defenceTeamWidthClass0.0160.0130.0000.1500.0000.1540.2040.1710.1650.5210.2910.9290.3110.0820.1150.1150.0690.1550.1610.1210.0000.4820.2411.0000.154
defenceDefenderLineClass0.0900.0360.0000.2030.2290.3910.1820.1230.2460.1940.3580.2030.3980.1620.1520.3290.2360.1530.0660.1810.1520.1330.3260.1541.000

Missing values

2023-02-28T13:31:08.377227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T13:31:08.898344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idteam_fifa_api_idteam_api_iddatebuildUpPlaySpeedbuildUpPlaySpeedClassbuildUpPlayDribblingbuildUpPlayDribblingClassbuildUpPlayPassingbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingchanceCreationPassingClasschanceCreationCrossingchanceCreationCrossingClasschanceCreationShootingchanceCreationShootingClasschanceCreationPositioningClassdefencePressuredefencePressureClassdefenceAggressiondefenceAggressionClassdefenceTeamWidthdefenceTeamWidthClassdefenceDefenderLineClass
91061485762010-02-22 00:00:0060BalancedNaNLittle40MixedOrganised45Normal35Normal55NormalOrganised30Deep70Double30NarrowOffside Trap
101161485762011-02-22 00:00:0065BalancedNaNLittle45MixedOrganised65Normal65Normal50NormalOrganised45Medium45Press50NormalCover
111261485762012-02-22 00:00:0059BalancedNaNLittle52MixedOrganised48Normal34Normal52NormalOrganised38Medium47Press53NormalCover
121361485762013-09-20 00:00:0059BalancedNaNLittle52MixedOrganised48Normal34Normal52NormalOrganised38Medium47Press53NormalCover
131461485762014-09-19 00:00:0059Balanced57.0Normal52MixedOrganised48Normal38Normal52NormalOrganised38Medium47Press53NormalCover
141561485762015-09-10 00:00:0059Balanced57.0Normal52MixedOrganised48Normal38Normal52NormalOrganised38Medium47Press53NormalCover
15164785642010-02-22 00:00:0045BalancedNaNLittle30ShortFree Form55Normal45Normal70LotsFree Form30Deep35Press60NormalOffside Trap
16174785642011-02-22 00:00:0065BalancedNaNLittle50MixedOrganised50Normal60Normal50NormalFree Form50Medium50Press50NormalOffside Trap
17184785642012-02-22 00:00:0045BalancedNaNLittle50MixedOrganised65Normal20Little50NormalFree Form45Medium45Press50NormalCover
18194785642013-09-20 00:00:0048BalancedNaNLittle54MixedOrganised51Normal53Normal64NormalFree Form48Medium49Press53NormalCover
idteam_fifa_api_idteam_api_iddatebuildUpPlaySpeedbuildUpPlaySpeedClassbuildUpPlayDribblingbuildUpPlayDribblingClassbuildUpPlayPassingbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingchanceCreationPassingClasschanceCreationCrossingchanceCreationCrossingClasschanceCreationShootingchanceCreationShootingClasschanceCreationPositioningClassdefencePressuredefencePressureClassdefenceAggressiondefenceAggressionClassdefenceTeamWidthdefenceTeamWidthClassdefenceDefenderLineClass
14291430174298682010-02-22 00:00:0030SlowNaNLittle30ShortOrganised45Normal35Normal70LotsOrganised45Medium50Press45NormalCover
14301431174298682011-02-22 00:00:0040BalancedNaNLittle58MixedOrganised56Normal54Normal49NormalOrganised46Medium52Press67WideCover
14311432174298682012-02-22 00:00:0060BalancedNaNLittle44MixedOrganised40Normal57Normal41NormalOrganised49Medium54Press50NormalCover
14321433174298682013-09-20 00:00:0060BalancedNaNLittle44MixedOrganised40Normal57Normal41NormalOrganised49Medium54Press50NormalCover
1444144524483942010-02-22 00:00:0030SlowNaNLittle30ShortFree Form50Normal35Normal70LotsOrganised40Medium30Contain45NormalOffside Trap
1445144624483942011-02-22 00:00:0043BalancedNaNLittle40MixedOrganised46Normal38Normal42NormalOrganised38Medium40Press53NormalCover
1446144724483942012-02-22 00:00:0034BalancedNaNLittle23ShortOrganised53Normal56Normal57NormalOrganised51Medium46Press61NormalCover
1447144824483942013-09-20 00:00:0038BalancedNaNLittle23ShortOrganised53Normal56Normal57NormalOrganised51Medium46Press61NormalCover
1448144924483942014-09-19 00:00:0038Balanced48.0Normal35MixedOrganised53Normal56Normal57NormalOrganised51Medium46Press61NormalCover
1449145024483942015-09-10 00:00:0052Balanced52.0Normal50MixedOrganised52Normal56Normal57NormalOrganised48Medium43Press49NormalCover